LMCA: a lightweight anomaly network traffic detection model integrating adjusted mobilenet and coordinate attention mechanism for IoT

Han, Dezhi; Zhou, HongXu; Weng, Tien-Hsiung; Wu, Zhongdai; Han, Bing; Li, Kuan-Ching; Pathan, Al-Sakib Khan

doi:10.1007/s11235-023-01059-5

Cited by 69 publications

(6 citation statements)

References 32 publications

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“…The IoT and industrial IoT play a significant role in today's world of intelligent networks, and they essentially use a WSN as a perception layer to collect the intended data [39]. This data is processed as Information and sent to cloud servers through a base station; the challenge here is the consumption of minimum energy for processing and communication [40,41].…”

Section: Related Workmentioning

confidence: 99%

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Ahmad,

Hussain,

Shah

et al. 2024

CMC

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Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these restrictions. Clustering may, therefore, be thought of as the best way to solve such issues. Consequently, it is crucial to analyze effective Cluster Head (CH) selection to maximize efficiency throughput, extend the network lifetime, and minimize energy consumption. This paper proposed an Accelerated Particle Swarm Optimization (APSO) algorithm based on the Low Energy Adaptive Clustering Hierarchy (LEACH), Neighboring Based Energy Efficient Routing (NBEER), Cooperative Energy Efficient Routing (CEER), and Cooperative Relay Neighboring Based Energy Efficient Routing (CR-NBEER) techniques. With the help of APSO in the implementation of the WSN, the main methodology of this article has taken place. The simulation findings in this study demonstrated that the suggested approach uses less energy, with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH, 1.003 to 0.0521 for 10 CH, and 0.1734 to 0.0911 for 15 CH. The sending packets ratio was also raised for all three CH selection scenarios, increasing from 659 to 1730. The number of dead nodes likewise dropped for the given combination, falling between 71 and 66. The network lifetime was deemed to have risen based on the results found. A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol. Similar to underwater, WSN can make use of the proposed protocol. The overall results have been evaluated and compared with the existing approaches of sensor networks.

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Section: Related Workmentioning

confidence: 99%

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Ahmad,

Hussain,

Shah

et al. 2024

CMC

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“…Inspired by related works [34][35][36][37][38], we expect our framework to be applied in spatiotemporal action detection and security surveillance.…”

Section: Yolo Trackermentioning

confidence: 99%

A Tracking-Based Two-Stage Framework for Spatio-Temporal Action Detection

Luo,

Yang,

Liu

et al. 2024

Electronics

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Spatio-temporal action detection (STAD) is a task receiving widespread attention and has numerous application scenarios, such as video surveillance and smart education. Current studies follow a localization-based two-stage detection paradigm, which exploits a person detector for action localization and a feature processing model with a classifier for action classification. However, many issues occur due to the imbalance between task settings and model complexity in STAD. Firstly, the model complexity of heavy offline person detectors adds to the inference overhead. Secondly, the frame-level actor proposals are incompatible with the video-level feature aggregation and Region-of-Interest feature pooling in action classification, which limits the detection performance under diverse action motions and results in low detection accuracy. In this paper, we propose a tracking-based two-stage spatio-temporal action detection framework called TrAD. The key idea of TrAD is to build video-level consistency and reduce model complexity in our STAD framework by generating action track proposals among multiple video frames instead of actor proposals in a single frame. In particular, we utilize tailored tracking to simulate the behavior of human cognitive actions and used the captured motion trajectories as video-level proposals. We then integrate a proposal scaling method and a feature aggregation module into action classification to enhance feature pooling for detected tracks. Evaluations in the AVA dataset demonstrate that TrAD achieves SOTA performance with 29.7 mAP, while also facilitating a 58% reduction in overall computation compared to SlowFast.

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“…Through multiple layers of surface feature learning, high-level feature representations of raw data are progressively obtained, eliminating the need for manual feature input and extraction. [ 2 ] exhibits remarkable advantages compared to traditional techniques, such as automatic feature extraction, impressive efficiency, simplicity, and higher accuracy. These networks, by processing large datasets, possess the ability to discern and learn complex features, thereby enhancing target recognition capabilities [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

YOLO-ISTD: An infrared small target detection method based on YOLOv5-S

Hao,

Wang,

et al. 2024

PLoS ONE

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Infrared target detection is widely used in industrial fields, such as environmental monitoring, automatic driving, etc., and the detection of weak targets is one of the most challenging research topics in this field. Due to the small size of these targets, limited information and less surrounding contextual information, it increases the difficulty of target detection and recognition. To address these issues, this paper proposes YOLO-ISTD, an improved method for infrared small target detection based on the YOLOv5-S framework. Firstly, we propose a feature extraction module called SACSP, which incorporates the Shuffle Attention mechanism and makes certain adjustments to the CSP structure, enhancing the feature extraction capability and improving the performance of the detector. Secondly, we introduce a feature fusion module called NL-SPPF. By introducing an NL-Block, the network is able to capture richer long-range features, better capturing the correlation between background information and targets, thereby enhancing the detection capability for small targets. Lastly, we propose a modified K-means clustering algorithm based on Distance-IoU (DIoU), called K-means_DIOU, to improve the accuracy of clustering and generate anchors suitable for the task. Additionally, modifications are made to the detection heads in YOLOv5-S. The original 8, 16, and 32 times downsampling detection heads are replaced with 4, 8, and 16 times downsampling detection heads, capturing more informative coarse-grained features. This enables better understanding of the overall characteristics and structure of the targets, resulting in improved representation and localization of small targets. Experimental results demonstrate significant achievements of YOLO-ISTD on the NUST-SIRST dataset, with an improvement of 8.568% in mAP@0.5 and 8.618% in mAP@0.95. Compared to the comparative models, the proposed approach effectively addresses issues of missed detections and false alarms in the detection results, leading to substantial improvements in precision, recall, and model convergence speed.

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LMCA: a lightweight anomaly network traffic detection model integrating adjusted mobilenet and coordinate attention mechanism for IoT

Cited by 69 publications

References 32 publications

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

A Tracking-Based Two-Stage Framework for Spatio-Temporal Action Detection

YOLO-ISTD: An infrared small target detection method based on YOLOv5-S

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